Abstract
This paper is concerned with integrating knowledge-based modeling or modeling from first principles, with data-driven or automated modeling of dynamic systems. The approach presented here includes methods for equation discovery: unlike mainstream system identification methods, which work under the assumption that the form of the equations is known, equation discovery systems explore a space of possible equation structures. We propose a formalism for representing knowledge about processes in population dynamics domains, and a method to transform such knowledge into an operational form that could be used by equation discovery systems. We also describe the extensions of the equation discovery system L agramge necessary to incorporate this kind of knowledge in the process of equation discovery.
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